I have five data points (A, B, C, D, E) in a two dimensional plane. Based on the euclidean distance between these points how can I group them?

I have five data points (A, B, C, D, E) in a two dimensional plane where three points (A, B, D) are close to each other and remaining two (C, E) are far from the group. If I calculate the distances between these points the results are

dist(A, B) = 0.3, dist(A, C) = 1.3, dist(A, D) = 0.15, dist(A, E) = 1.0, dist(B, C) = 0.9, dist(B, D) =0.2, dist(B, E) = 1.1, dist(C, D)=1.6, dist(C, E) = 1.0, dist(D, E) = 1.5.

Now if I choose a distance threshold as 0.6, I need to get a result as:

A, B, D (Closer to each other)

C (far from other points)

E (far from other points)

Answer 1

[KSSV]

Read about kmeans.

[Shaik Ahmad]

Dear @KSSV thank you for your response.

I read about kmeans, there I need to mention the number of clusters. I am searching for a solution where the number of clusters are calculated automatically by using a predefined distance threshold.

[KSSV]

Yes...for kmeans you need to provide the number of clusters. Why don't you give your points. Let me give a trry.

[Shaik Ahmad]

I found a solution for that.

I need to apply Hierarchical Clustering to get that output. Here There are functions called pdist and linkage which do the work.

Thank you for yout support @KSSV